Using common subexpressions to optimize multiple queries

Abstract

This paper deals with the problem of identifying common subexpressions and using them in the simultaneous optimization of multiple queries. In particular, we emphasize the strateg.v of selecting access plans ror the single queries and their integration into a global access plan that takes advantage of common tasks. We present a dynamic programming algorithm ror the selection or individual access plans such that the resulting global access plan is or minimum processing cost. The computational complexity of this algorithm represents a significant improvement over existing algorithms.

Multi Query Optimization Algorithm Using Semantic and Heuristic Approaches

“…Most of the multi query optimization algorithms do not take into consideration possibilities of sub-expressions that may be common and save more in overall costs except in reuse based optimization sharing possibilities are explored [6]. These algorithms are Basic Volcano, Volcano SH , and Volcano RU use the same principal though with different philosophies.…”

Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption

“…Dynamic programming algorithm has been proposed to select the access plan for each query considering the sharing of subexpressions, and achieve the minimum processing cost [12]. The concept of subexpression is similar to filter, but it is a fix task of the access plan for a query other than a probabilistic condition.…”

Query Optimization and Planning in Object-Oriented Knowledge Bases

“…In general, multiple-4uery optimization procedures consist of two parts [PaSe88]: identifying common sub-expressions and constructing a global access plan. Although detection of common sub-expressions or applicability of access paths may be computationally intractable or even undecidable if a set of arbitrary subexpressions is considered [Jark84] , for which the major concern for nested queries has been the treatment of aggregation functions.…”